scholarly journals A New Contrast-Enhancing Feature for Cloud Detection in Ground-Based Sky Images

2015 ◽  
Vol 32 (2) ◽  
pp. 209-219 ◽  
Author(s):  
Vijai T. Jayadevan ◽  
Jeffrey J. Rodriguez ◽  
Alexander D. Cronin
Keyword(s):  
Imaging System ◽  
Color Image ◽  
Low Cost ◽  
Color Space ◽  
Cloud Detection ◽  
Hsv Color Space ◽  
Detection Algorithms ◽  
Camera Systems ◽  
New Feature ◽  
Thresholding Algorithm

AbstractFor this study a ground-based sky imaging system was developed that, unlike most other such systems, consists of a low-cost sun-tracking camera fitted with a fish-eye lens. The application of interest is short-term solar power forecasting, so cloud detection is an important step. The hybrid thresholding algorithm proposed by Li et al. for cloud detection is employed. Most cloud detection algorithms make use of the red and blue components in a color image. Though these features perform well for many images, they do not produce good results for the images in this study due to the insufficient contrast between cloud and sky pixels when using ratios between red and blue. To overcome this issue, a new feature, the normalized saturation/value (NSV) ratio, is proposed that is computed in the hue–saturation–value (HSV) color space. This study shows that the NSV ratio produces good contrast between cloud and sky pixels not only for the images in this study but also for general sky images acquired using different camera systems. The reasoning behind the choice of the new ratio is described, and quantitative and qualitative results are presented.

scholarly journals Interactive Color Image Segmentation using HSV Color Space

2019 ◽  
Vol 7 (1) ◽  
pp. 37-41
Author(s):  
D. Hema ◽  
◽  
Dr. S. Kannan ◽  
Keyword(s):  
Image Segmentation ◽  
Object Detection ◽  
Color Image ◽  
Research Work ◽  
Color Space ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Space Model ◽  
Detection Algorithms ◽  
Rgb Image

The primary goal of this research work is to extract only the essential foreground fragments of a color image through segmentation. This technique serves as the foundation for implementing object detection algorithms. The color image can be segmented better in HSV color space model than other color models. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.

Content-Based Image Retrieval Using Color Volume Histograms

2019 ◽  
Vol 33 (11) ◽  
pp. 1940010 ◽  
Author(s):  
Ji-Zhao Hua ◽  
Guang-Hai Liu ◽  
Shu-Xiang Song
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Content Based Image Retrieval ◽  
Feature Descriptor ◽  
Human Visual Perception ◽  
Hsv Color Space ◽  
Spatial Features ◽  
Close Relationship ◽  
New Feature

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15[Formula: see text]000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

The Study of Forest Fire Color Image Segmentation

2011 ◽  
Vol 474-476 ◽  
pp. 2140-2145
Author(s):  
Si Li ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Segmentation Methods ◽  
Time Periods ◽  
Night And Day

Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

scholarly journals Multifocus Color Image Fusion Based on NSST and PCNN

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Jin ◽  
Rencan Nie ◽  
Dongming Zhou ◽  
Quan Wang ◽  
Kangjian He
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Color Image ◽  
Color Space ◽  
Color Spaces ◽  
Fusion Algorithm ◽  
Hsv Color Space ◽  
Coupled Neural Networks ◽  
Different Color ◽  
Pulse Coupled Neural Networks

This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed to get the RGB color image. The experimental results indicate that the proposed color image fusion algorithm is more efficient than other common color image fusion algorithms.

Color image fusion based on simplified pulse coupled neural network and HSV color space

2016 ◽  
Author(s):  
Jin Xin ◽  
Dongming Zhou ◽  
Shaowen Yao ◽  
Rencan Nie ◽  
Chuanbo Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Color Image ◽  
Color Space ◽  
Hsv Color Space ◽  
Pulse Coupled Neural Network

scholarly journals Enhancement Image Intensity of HSV Color Space

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2583-2585
Keyword(s):  
Light Intensity ◽  
Face Detection ◽  
Color Image ◽  
Color Space ◽  
Detection Performance ◽  
Lighting Condition ◽  
Hsv Color Space ◽  
Number Of Faces ◽  
Intensity Normalization ◽  
Poor Lighting

One of factor that affects technology in face detecting or recognizing is illumination. Poor lighting can cause difficulty to the system to recognize face. Although it is over exposure or under exposure. By doing color image processing, it supports the system to detect face in a poor lighting condition. This research used lighting intensity normalization method to increase face detection performance. This method can normalize the light intensity especially on the face lighting. We normalize the light intensity through HSV color space. HSV color space has saturation which is amount of light in the image. The method proceed saturation in image to increase face detection performance. Total number of faces we had tested is 286 faces, the system detect 243 faces before intensity normalization proceed. Whereas, after normalization process, it detects more faces which is 279 faces. As we can see, the percentage improvement before to after intensity normalization is 84.97% to 97.55%. This is 12.58% improvement. We can say this method helps face detection to increase it performance.

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